NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL)

The NCA Generative AI LLMs certification is an entry-level credential that validates the foundational concepts for developing, integrating, and maintaining AI-driven applications using generative AI and large language models (LLMs) with NVIDIA solutions.

Candidate audience includes:

  • AI DevOps engineers
  • AI strategists
  • Applied data scientists
  • Applied data research engineers
  • Applied deep learning research scientists
  • Cloud solution architects
  • Data scientists
  • Deep learning performance engineers
  • Generative AI specialists
  • LLM specialists/researchers
  • Machine learning engineers
  • Senior researchers
  • Software engineers
  • Solutions architects


Learners should have a basic understanding of generative AI and large language models.

Recommended training for this certification

  • Generative AI Explained (self-paced course, 2 hours, free)
  • Getting Started With Deep Learning (self-paced course, 8 hours) or Fundamentals of Deep Learning (instructor-led workshop, 8 hours)
  • Fundamentals of Accelerated Data Science (instructor-led workshop, 8 hours)
  • Introduction to Transformer-Based Natural Language Processing (self-paced course, 6 hours)
  • Building Transformer-Based Natural Language Processing Applications (instructor-led workshop, 8 hours)
  • Rapid Application Development Using LLMs (instructor-led workshop, 8 hours)
  • Efficient Large Language Model (LLM) Customization (instructor-led workshop, 8 hours)
  • Prompt Engineering With LLaMA-2 (self-paced course, 3 hours)
  • Augmenting Your LLM Using Retrieval-Augmented Generation (self-paced course, 1 hour, free)
  • Building RAG Agents With LLMs (self-paced course, 8 hours, free) or Building RAG Agents with LLMs (instructor-led workshop, 8 hours)


Certification Exam Details

  • Duration: One hour
  • Price: $135
  • Certification level: Associate
  • Subject: Generative AI and large language models
  • Number of questions: 50
  • Language: English

Topics covered in the exam include:

  • Fundamentals of machine learning and neural networks
  • Prompt engineering
  • Alignment
  • Data analysis and visualization
  • Experimentation
  • Data Preprocessing and feature engineering
  • Experiment design
  • Software development
  • Python libraries for LLMs
  • LLM integration and deployment


This certification is valid for two years from issuance. Recertification may be achieved by retaking the exam.